System and method for determining effective road grade characteristic

ABSTRACT

A vehicle system includes a computer in a vehicle, and the computer includes a processor and a memory. The computer is configured to determine an operational state of a vehicle, calculate a road grade value from data from a first sensor, calculate an effective road grade value from a torque demand value, update modeling variables to map the road grade value substantially to the effective grade value when the operation state is determined to be a steady-state condition, and calculate a road grade characteristic with the road grade value and the modeling variables.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent Appl. No.61/915,365, filed Dec. 12, 2013 entitled “Rule Based Smart CruiseControl”, the complete contents of which is hereby incorporated hereinby reference in its entirety.

BACKGROUND

Vehicle control operations, such as automobile cruise control, maydepend upon environmental conditions and related forces affecting themotion of the vehicle. For example, road grade is an environmentalcondition that may oppose or promote the longitudinal motion of avehicle, and, therefore, a control operation such as cruise control mayadjust according to the resultant effect from a particular road grade onthe vehicle. Other conditions that may affect the motion of a vehicle,alone or in combination with road grade and each other, include forcesacting on the vehicle from headwinds, changes in vehicle mass, and otheraerodynamic effects. It is desirable, but currently difficult, for avehicle control system to accurately and efficiently model a total ofsuch forces acting on the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary vehicle system for determining aneffective road grade characteristic.

FIG. 2 is a flowchart of one exemplary process that may be implementedby the vehicle system.

DETAILED DESCRIPTION

Road grade is one of many conditions with associated forces that mayoppose or promote the longitudinal motion of a vehicle. To account forunmeasured forces—such as headwinds, changes in vehicle mass, unknownaerodynamic effects, etc.—in addition to road grade, an effective roadgrade can be calculated or modeled. In applications such as cruisecontrol, knowledge of the unknown resistive forces opposing vehiclemotion are critical for velocity profile planning.

According to this disclosure, during steady-state power-on maneuvers,the effective grade can be computed from the difference between themeasured, sensed or estimated torque and the known torque required tomaintain the vehicle at steady-state on a flat road. During transienttorque or power-off maneuvers, the force balance is non-zero, therefore,the effective grade cannot be efficiently computed from the estimatedtorque, e.g., such a calculation would require a much more complex modelto capture all of the dynamic effects, that would, therefore, berelatively more susceptible to errors, and/or an estimate of vehicleacceleration through a relatively more complex computer operation, suchas numerically differentiating the velocity of vehicle. According tothis disclosure, road grade is also computed, during all conditions,through an accelerometer-based measurement, and the accelerometer-basedroad grade and the measured torque-based effective grade are bothutilized through an online mapping or update of a road gradecharacteristic, this model is updated based on the effective gradecalculation during steady-state, power on maneuvers.

FIG. 1 schematically illustrates an exemplary vehicle 100. The exemplarysystem may take many different forms and include multiple and/oralternate components and facilities. It is to be understood that theexemplary components illustrated are not intended to be limiting, andthat additional or alternative components and/or implementations may beused. For example, the vehicle 100 may be any passenger or commercialvehicle such as a car, a truck, sport-utility vehicle, a bus, train, aboat, or an airplane.

With further reference to FIG. 1, an exemplary vehicle 100 includes avehicle computing device or computer 105 that generally includes aprocessor and a memory, the memory including one or more forms ofcomputer-readable media, and storing instructions executable by theprocessor for performing various operations, including as disclosedherein. The computer 105 of the vehicle 100 receives information, e.g.,collected data, from one or more data collectors 110 related to variouscomponents or conditions of the vehicle 100, e.g., components such as anaccelerometer sensor system, a torque sensor system, a braking system, asteering system, a powertrain, etc., and/or conditions such as vehicle100 torque demand, speed, acceleration, pitch, yaw, roll, etc. Thecomputer 105 may include more than one computing device, e.g.,controllers or the like included in the vehicle 100 for monitoringand/or controlling various vehicle components, e.g., a controller module106, an engine control unit (ECU), transmission control unit (TCU), etc.The computer is generally configured for communications on a controllerarea network (CAN) bus or the like. The computer may also have aconnection to an onboard diagnostics connector (OBD-II). Via the CANbus, OBD-II, and/or other wired or wireless mechanisms, the computer maytransmit messages to various devices in a vehicle and/or receivemessages from the various devices, e.g., controllers, actuators,sensors, etc. Alternatively or additionally, in cases where the computeractually comprises multiple devices, the CAN bus or the like may be usedfor communications between the multiple devices that comprise thevehicle computer. In addition, the computer may be configured forcommunicating with a network, which may include various wired and/orwireless networking technologies, e.g., cellular, Bluetooth, wiredand/or wireless packet networks, etc.

Generally included in instructions stored in and executed by thecomputer 105 is a controller module 106. Using data received in thecomputer 105, e.g., from data collectors 110, data included as storedparameters 116, etc., the module 106 may control various vehicle 100systems or equipment. For example, the module 106 may be used toaccelerate, decelerate or maintain the velocity of vehicle 100, such asin conjunction with a cruise-control operation of vehicle 100.

Data collectors 110 may include a variety of devices. For example,various controllers in a vehicle may operate as data collectors 110 toprovide data 115 via the CAN bus, e.g., data 115 relating to torquedemand and/or output, vehicle speed, acceleration, etc. Further, sensorsor the like, global positioning system (GPS) equipment, etc., could beincluded in a vehicle and configured as data collectors 110 to providedata directly to the computer 105, e.g., via a wired or wirelessconnection. Sensor data collectors 110 could include communicationdevices to send and receive information from other vehicles, such aspath intentions from vehicles surrounding vehicle 100. Sensor datacollectors 110 could include mechanisms such as RADAR, LADAR, sonar,etc. sensors that could be deployed to measure a distance between thevehicle 100 and other vehicles or objects. Yet other sensor datacollectors 110 could include accelerometer sensors. In addition, datacollectors 110 may include sensors to detect a position, change inposition, rate of change in position, etc., of vehicle 100 componentssuch as a steering wheel, brake pedal, accelerator, gearshift lever,etc.

A memory of the computer 105 generally stores collected data 115.Collected data 115 may include a variety of data collected in a vehicle100. Examples of collected data 115 are provided above, and moreover,data 115 is generally collected using one or more data collectors 110,and may additionally include data calculated therefrom in the computer105. In general, collected data 115 may include any data that may begathered by a collection device 110 and/or computed from such data.Accordingly, collected data 115 could include a variety of data relatedto vehicle 100 operations and/or performance, data received from anothervehicle, as well as data related to environmental conditions, roadconditions, etc. relating to the vehicle 100. For example, collecteddata 115 could include data concerning a vehicle 100 torque demand,measured or sensed torque, position, speed, acceleration, pitch, yaw,roll, braking, presence or absence of precipitation, tire pressure, tirecondition, etc.

A memory of the computer 105 may further store parameters 116. Aparameter 116 generally governs control of a system or component ofvehicle 100. These parameters may vary due to an environmentalcondition, road condition, vehicle 100 condition, or the like. Forexample, a parameter 116 may specify expected torque demands for vehicle100 under certain conditions, e.g. a flat vehicle path, a certainvehicle weight or mass, to enable comparison of demanded or outputtorque during operation of vehicle 100 towards modeling or calculatingthe conditions in which vehicle 100 is operating.

As discussed in further detail below, the computer 105 and/or controllermodule 106 may compute or model an updated effective road gradecharacteristic, which can be stored as one of parameters 116, through anonline mapping incorporating an accelerometer-based road gradecalculation and, during steady-state and power-on maneuvers, atorque-based calculation of effective grade computed from the differencebetween the measured, sensed or estimated torque and the known torquerequired to maintain the vehicle at steady-state on a flat road, savedas a stored parameter 116. In one particular example, the effective roadgrade characteristic is determined by the computer 105 with theaccelerometer-based road grade calculation and modeling variables orparameters, i.e. road grade characteristic model coefficients, each ofwhich may be stored as part of parameters 116. In this example, themodeling variables, i.e. road grade characteristic model coefficients,are updated by the computer 105 during steady-state and power-onoperating conditions of vehicle 100 according to the torque-basedeffective grade calculation, which may also be stored as one ofparameters 116. The online update of the modeling variables isswitched-off, or otherwise not performed, when the effective gradeestimate is inaccurate, i.e. during transient maneuvers, and, therefore,the last update of the modeling variables is used to continue todetermine the effective road grade characteristic, based on theaccelerometer based grade calculation, during transient conditions.Updating the modeling variables allows the method to account forvariation in the unmeasured forces over time. As such, according to thisdisclosure, the effective road grade characteristic may be calculatedand updated throughout operation of vehicle 100, while including atleast an estimate of the effective grade—and therefore the unmeasuredforces affecting vehicle 100 in addition to the actual road gradeitself—as well as avoiding relatively more complex derivativeoperations.

In one example implementation, the torque-based calculation of thecomputer 105 is identified where k is the time-step, m(k) is the vehiclemass, a(k) is the vehicle acceleration, T(k) is the torque demand, r isthe effective radius, V(k) is the vehicle velocity, P_(SS)(V (k)) is thesteady-state power required to maintain the vehicle velocity V(k), g isthe acceleration due to gravity, δ_(true)(k) is the road grade andF_(u)(k) is the sum of all unmeasured forces in the plane, e.g.head/tail winds. True road grade δ_(true)(k) can be obtained from theforce balance in the forward path of a vehicle, specifically, the forcebalance is

$\begin{matrix}{{{m(k)}{a(k)}} = {\frac{T(k)}{r} + \frac{P_{ss}\left( {V(k)} \right)}{V(k)} + {{m(k)}g\;{\sin\left( {\delta_{true}(k)} \right)}} + {{F_{u}(k)}.}}} & (1)\end{matrix}$In this exemplary implementation, with the range of road grade beingsmall, the small angle theorem, namely, sin (δ_(true)(k))≈δ_(true)(k) isapplied; therefore equation (1) can be rewritten as

$\begin{matrix}{{{m(k)}{a(k)}} = {\frac{T(k)}{r} + \frac{P_{ss}\left( {V(k)} \right)}{V(k)} + {{m(k)}g\;{\delta_{true}(k)}} + {{F_{u}(k)}.}}} & (2)\end{matrix}$The true road grade can be calculated by rearranging (2) as

$\begin{matrix}{{\delta_{true}(k)} = {{- \frac{T(k)}{{m(k)}{gr}}} - \frac{P_{ss}\left( {V(k)} \right)}{V(k)} - {F_{u}(k)} + {{m(k)}{{a(k)}.}}}} & (3)\end{matrix}$Assuming a steady-state condition (m(k)a(k)=0), a nominal vehicle massm_(n), i.e. the curb weight and the standard occupant weight of vehicle100 stored as one of parameters 116, and that F_(u) (k) is unknown,equation (3) can be written in terms of effective grade δ_(τ)(k) as,

$\begin{matrix}{{\delta_{\tau}(k)}\overset{\Delta}{=}{{- \frac{T(k)}{m_{n}{gr}}} - {\frac{P_{ss}\left( {V(k)} \right)}{{V(k)}m_{n}g}.}}} & (4)\end{matrix}$Accordingly, the computer 105 and/or controller module 106 of thevehicle 100 computes an effective road grade characteristic as afunction of the torque demand T(k), which is retrieved or estimated fromcollected data 115, values for P_(SS)(V(k)), V(k), m_(n), g and rretrieved from collected data 115, saved as stored parameters 116,and/or derived from collected data 115 and/or parameters 116.

The effective grade equation (4) can be written as a sum of the trueroad grade and the unknown forces F_(u)(k) modeled as grade δ_(unmodeled)(k), specifically:δ_(τ)(k)=δ_(true)(k)+δ _(unmodeled)(k)  (5)where δ _(unmodeled)(k) is the effective road grade increase/decreasethrough F_(u)(k) and the difference between m(k) and m_(n),specifically,

$\begin{matrix}{{{\overset{\_}{\delta}}_{unmodeled}(k)} = {{\left\lbrack {\frac{1}{m(k)} - \frac{1}{m_{n}}} \right\rbrack\left\lbrack {\frac{T(k)}{gr} + \frac{P_{ss}\left( {V(k)} \right)}{{V(k)}g}} \right\rbrack} + {{F_{u}(k)}.}}} & (6)\end{matrix}$As such, F_(u)(k) and the true vehicle mass m_(n) may be determined fromthe effective grade δ_(τ)(k).

This exemplary method for computing δ_(τ)(k) does not cover transient orpower-off conditions of operation of vehicle 100, since the conditionm(k)a(k)=0 is not applicable. In this exemplary implementation, thecomputer 105 and/or module 106 of vehicle 100 estimates anaccelerometer-based grade δ_(a)(k), which is affine proportional toδ_(true)(k), through collected data 115 from an accelerometer sensor110. However, this determination does not take into account anyinformation about m or F_(u)(k).

For example, to derive road grade from vehicle speed and longitudinalacceleration measurements among collected data 115, the accelerometerdata in the longitudinal axis is integrated and compared to the truevehicle speed, and any difference between the values is proportional tograde. For a flat grade, the integral of the accelerometer data in thelongitudinal axis is equal to the vehicle velocity. On uphill grades,since a component of acceleration due to gravity is opposing the forwardacceleration of the vehicle, the integral of accelerometer data in thelongitudinal axis will result in a velocity lower than the true vehiclespeed. The difference is proportional to the road grade. For downhillgrade, the integral of the sensor data will result in a higher vehiclespeed estimate than the true vehicle speed since the component ofgravity is acting in the same direction as the vehicle motion. Again thedifference is proportional to the road grade. However, the true vehiclespeed, and accelerometer data are unaffected by vehicle mass andunmodeled forces (e.g. wind); therefore such calculations of road gradeare independent of such additional variables.

Accordingly, in this exemplary implementation, the corrected effectivegrade, or updated road grade characteristic, {circumflex over(δ)}_(τ)(k) is defined as{circumflex over (δ)}_(τ)(k)

α(k)φ(k),  (7)where the regressor φ

[1 δ_(a)(k)]^(T) and the model coefficients α(k)

[α₀(k) α₁(k)]. As set forth below, according to this disclosure, theparameters a(k) are determined such that during the time intervals whenm(k)a(k)=0, ∥{circumflex over (δ)}_(τ)(k)−δ_(τ)(k)∥ is small. Whenm(k)a(k) is nonzero, i.e. non steady state conditions, the mapping a(k)is not updated, and α(k) is updated when the vehicle is in steady-state.

In this example, the mapping update switch variable is β(k)ε{0, 1}.Specifically, β(k)=1 when m(k)a(k)=0. Furthermore, when m(k)a(k) is notequal to zero, β(k)=0.

Next, α(k) is determined using, for example, recursive least squares,that is,

$\begin{matrix}{{\alpha(k)} = \left\{ \begin{matrix}{{{\alpha\left( {k - 1} \right)} + {{L(k)}\left\lbrack {{\delta_{\tau}(k)} - {{\alpha\left( {k - 1} \right)}{\phi(k)}}} \right\rbrack}},} & {{{\beta(k)} = 1},} \\{{\alpha\left( {k - 1} \right)},} & {{{\beta(k)} = 0},}\end{matrix} \right.} & (8)\end{matrix}$where the variable L(k) is defined as:

$\begin{matrix}{{{L(k)}\overset{\Delta}{=}\frac{{P\left( {k - 1} \right)}{\phi(k)}}{\lambda + {{\phi^{T}(k)}{P\left( {k - 1} \right)}{\phi(k)}}}},} & (9)\end{matrix}$and P(0)ε

^(2×2) is positive definite and λε(0, 1] is the forgetting factor. Whenβ(k)=1, the map is updated, whereas, when β(k)=0, the model is fixed.P(k) is updated by

$\begin{matrix}{{P(k)} = \left\{ {\begin{matrix}{{\frac{1}{\lambda}\left( {{P\left( {k - 1} \right)} - \frac{{P\left( {k - 1} \right)}{\phi(k)}{\phi^{T}(k)}{P\left( {k - 1} \right)}}{\lambda + {{\phi^{T}(k)}{P\left( {k - 1} \right)}{\phi(k)}}}} \right)},} & {{{\beta(k)} = 1},} \\{{P\left( {k - 1} \right)},} & {{{\beta(k)} = 0},}\end{matrix}\mspace{20mu}{where}} \right.} & (10) \\{\mspace{79mu}{{{P(0)} = \begin{bmatrix}\gamma_{1} & 0 \\0 & \gamma_{2}\end{bmatrix}},{{and}\mspace{14mu}\gamma_{1}},{\gamma_{2} > 0.}}} & (11)\end{matrix}$

Accordingly, as set forth in this example implementation, the vehicle100 may utilize the computer 105 and/or controller module 106 to computeand update an effective road grade characteristic, based on anaccelerometer-based road grade calculation and the existing mappingvariables during transient and power-off conditions and, duringsteady-state and power-on maneuvers, incorporating a torque-basedcalculation of effective grade into the updated mapping variables. Assuch, according to this disclosure, the effective road grade may becalculated and updated throughout operation of vehicle 100 whileavoiding potentially more complex derivative operations. It should beunderstood, that the particular calculations and models are exemplary.For example, the model may be updated using any least-squares methodsuch as batch, or any method for solving a linear system of equations.Furthermore, in this implementation, the model was chosen to be a line.The model could be any structure which provides good residual error, forexample, nonlinear (polynomial, radial basis, etc.), dynamic or both.

In general, computing systems and/or devices, such as the computer 105and/or controller module 106 of the vehicle 100, may employ any of anumber of computer operating systems, including, but by no means limitedto, versions and/or varieties of the Ford SYNC® operating system, theMicrosoft Windows® operating system, the Unix operating system (e.g.,the Solaris® operating system distributed by Oracle Corporation ofRedwood Shores, Calif.), the AIX UNIX operating system distributed byInternational Business Machines of Armonk, N.Y., the Linux operatingsystem, the Mac OS X and iOS operating systems distributed by Apple Inc.of Cupertino, Calif., and the Android operating system developed by theOpen Handset Alliance. Examples of computing devices include, withoutlimitation, a vehicle computer or control unit, a computer workstation,a server, a desktop, notebook, laptop, or handheld computer, or someother computing system and/or device.

Computing devices generally include computer-executable instructions,where the instructions may be executable by one or more computingdevices such as those listed above. Computer-executable instructions maybe compiled or interpreted from computer programs created using avariety of programming languages and/or technologies, including, withoutlimitation, and either alone or in combination, Java™, C, C++, VisualBasic, Java Script, Perl, etc. In general, a processor (e.g., amicroprocessor) receives instructions, e.g., from a memory, acomputer-readable medium, etc., and executes these instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein. Such instructions and other data may be stored andtransmitted using a variety of computer-readable media.

A computer-readable medium (also referred to as a processor-readablemedium) includes any non-transitory (e.g., tangible) medium thatparticipates in providing data (e.g., instructions) that may be read bya computer (e.g., by a processor of a computer). Such a medium may takemany forms, including, but not limited to, non-volatile media andvolatile media. Non-volatile media may include, for example, optical ormagnetic disks and other persistent memory. Volatile media may include,for example, dynamic random access memory (DRAM), which typicallyconstitutes a main memory. Such instructions may be transmitted by oneor more transmission media, including coaxial cables, copper wire andfiber optics, including the wires that comprise a system bus coupled toa processor of a computer. Common forms of computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, any other magnetic medium, a CD-ROM, DVD, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any othermemory chip or cartridge, or any other medium from which a computer canread.

Databases, data repositories or other data stores described herein mayinclude various kinds of mechanisms for storing, accessing, andretrieving various kinds of data, including a hierarchical database, aset of files in a file system, an application database in a proprietaryformat, a relational database management system (RDBMS), etc. Each suchdata store is generally included within a computing device employing acomputer operating system such as one of those mentioned above, and areaccessed via a network in any one or more of a variety of manners. Afile system may be accessible from a computer operating system, and mayinclude files stored in various formats. An RDBMS generally employs theStructured Query Language (SQL) in addition to a language for creating,storing, editing, and executing stored procedures.

In some examples, system elements may be implemented ascomputer-readable instructions (e.g., software) on one or more computingdevices (e.g., servers, personal computers, etc.), stored on computerreadable media associated therewith (e.g., disks, memories, etc.). Acomputer program product may comprise such instructions stored oncomputer readable media for carrying out the functions described herein.

FIG. 2 is a flowchart of an exemplary process 200 that may beimplemented by the computer 105 and/or controller module 106 of thevehicle 100 to determine an updated effective road grade characteristic,stored among parameters 116, for use, e.g., in a cruise-controloperation.

At a block 205, the vehicle 100 may receive information from one or moresensors 110, such as velocity of the vehicle 100, acceleration of thevehicle 100, towards determining the operational state of vehicle 100.At a block 210, the computer 105 may receive accelerometer sensor datafrom collected data 115. Next, at a block 215, the computer 105 maycalculate road grade according to the accelerometer sensor data. Forexample, as set forth above, the road grade can be determined from thedifference between the integral of the accelerometer data along thelongitudinal axis of vehicle 100 and the true vehicle speed, which maybe measured by one of the sensors 110. According to the informationcollected at the block 205, next, at a block 220, the computer 105 maydetermine whether the vehicle 100 is operating in a steady-statecondition, i.e. maintaining a substantially constant velocity, or isotherwise in a transient condition, e.g. accelerating or decelerating.

If the vehicle 100 is not in a steady-state condition, at block 225,computer 105 skips or suspends updating of the modeling variables orroad grade characteristic model coefficients, such as set forth in theexemplary model or mapping herein.

If the vehicle 100 is in a steady-state condition, at block 230, thecomputer 105 may receive torque demand data, such as information fromsensors 110, such as, for example, data from a torque sensor, or mayreceive a torque demand estimate based on other sensors 110. Next, at ablock 235, the computer 105 compares the torque demand data collected tostored torque requirements among parameters 116. Next, at block 240, thecomputer 105 may calculate an effective road grade according to thecomparison of the measured or estimated torque demand data saved as orderived from collected data 115 and the stored torque requirements amongparameters 116. Next, at a block, 245, the computer 105 may update theone or more road grade characteristic model coefficients, or modelingvariables, based on the calculated effective grade, such as set forth inthe exemplary implementation herein.

Next, with the updated one or more modeling variables (i.e. road gradecharacteristic model coefficients) from block 245, or having suspendedthe update of the road grade characteristic model coefficients at block225, the road grade characteristic is updated at a block 250. After thecalculation of the road grade characteristic at the block 250, thecomputer 105 determines whether it is to continue updating the roadgrade characteristic at the block 260. If so, e.g. the vehicle 100 iscontinuing to operate in cruise-control mode, the process 200 returns toblock 205. If not, e.g. the vehicle 100 has reached its destination, theprocess 200 ends.

With regard to the processes, systems, methods, heuristics, etc.described herein, it should be understood that, although the steps ofsuch processes, etc. have been described as occurring according to acertain ordered sequence, such processes could be practiced with thedescribed steps performed in an order other than the order describedherein. It further should be understood that certain steps could beperformed simultaneously, that other steps could be added, or thatcertain steps described herein could be omitted. In other words, thedescriptions of processes herein are provided for the purpose ofillustrating certain embodiments, and should in no way be construed soas to limit the claims.

Accordingly, it is to be understood that the above description isintended to be illustrative and not restrictive. Many embodiments andapplications other than the examples provided would be apparent uponreading the above description. The scope should be determined, not withreference to the above description, but should instead be determinedwith reference to the appended claims, along with the full scope ofequivalents to which such claims are entitled. It is anticipated andintended that future developments will occur in the technologiesdiscussed herein, and that the disclosed systems and methods will beincorporated into such future embodiments. In sum, it should beunderstood that the application is capable of modification andvariation.

All terms used in the claims are intended to be given their broadestreasonable constructions and their ordinary meanings as understood bythose knowledgeable in the technologies described herein unless anexplicit indication to the contrary in made herein. In particular, useof the singular articles such as “a,” “the,” “said,” etc. should be readto recite one or more of the indicated elements unless a claim recitesan explicit limitation to the contrary.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

The invention claimed is:
 1. A system comprising a computer in avehicle, the computer comprising a processor and a memory, wherein thecomputer is configured to: determine an operational state of a vehicle;calculate a road grade value from data from an accelerometer; calculatean effective grade value from a torque demand value; determine acorrection coefficient to adjust the road grade value to substantiallymatch the effective grade value when the operation state is determinedto be a steady-state condition; determine a corrected effective gradebased at least in part on the correction coefficient and the road gradevalue; and adjust a velocity of the vehicle with a cruise control systembased at least in part on the corrected effective grade when theoperation state is a transient condition; wherein the correctioncoefficient is based at least in part on the road grade value and theeffective grade value.
 2. The vehicle system of claim 1, wherein thecomputer is further configured to estimate the torque demand value fromcollected data.
 3. The vehicle system of claim 2, wherein the collecteddata includes at least one of vehicle speed data, requested torque data,and vehicle acceleration data.
 4. The vehicle system of claim 1, whereinthe road grade value is calculated from data from the accelerometer anda vehicle speed sensor.
 5. The vehicle system of claim 1, wherein thecorrection coefficient is determined with a least-squares calculation.6. A method comprising: determining an operational state of a vehicle;calculating a road grade value from data from an accelerometer;calculating an effective grade value from a torque demand value;determining a correction coefficient to adjust the road grade value tosubstantially match the effective grade value when the operation stateis determined to be a steady-state condition, determining a correctedeffective grade based at least in part on the correction coefficient andthe road grade value; and adjusting a velocity of the vehicle with acruise control system based at least in part on the corrected effectivegrade when the operation state is a transient condition; wherein thecorrection coefficient is based at least in part on the road grade valueand the effective grade value.
 7. The method of claim 6, furthercomprising estimating the torque demand value from collected data. 8.The method of claim 7, wherein the collected data includes at least oneof vehicle speed data, requested torque data, and vehicle accelerationdata.
 9. The method of claim 6, wherein the road grade value iscalculated from data from the accelerometer and a vehicle speed sensor.10. The method of claim 6, where the correction coefficient isdetermined with a least-squares calculation.
 11. A non-transitorycomputer-readable medium tangibly embodying computer-executableinstructions that cause a processor to execute operations comprising:determining an operational state of a vehicle; calculating a road gradevalue from data from an accelerometer; calculating an effective gradevalue from a torque demand value; determining a correction coefficientto adjust the road grade value to substantially match the effectivegrade value when the operation state is determined to be a steady-statecondition, determining a corrected effective grade based at least inpart on the correction coefficient and the road grade value; andadjusting a velocity of the vehicle with a cruise control system basedat least in part on the corrected effective grade when the operationstate is a transient condition; wherein the correction coefficient isbased at least in part on the road grade value and the effective gradevalue.
 12. The non-transitory computer-readable medium of claim 11, theoperations further comprising estimating the torque demand value fromcollected data.
 13. The non-transitory computer-readable medium of claim12, wherein the collected data includes at least one of vehicle speeddata, requested torque data, and vehicle acceleration data.
 14. Thenon-transitory computer-readable medium of claim 11, wherein the roadgrade value is calculated from data from the accelerometer and a vehiclespeed sensor.
 15. The non-transitory computer-readable medium of claim11, wherein the correction coefficient is determined with aleast-squares calculation.